CN113535672A - Turbidity data processing method based on autonomous underwater robot platform - Google Patents
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Abstract
The invention belongs to the field of turbidity data processing of an ocean motion platform, and particularly relates to a turbidity data processing method based on an autonomous underwater robot platform. The invention comprises the following steps: carrying out detection movement on a planned line by an autonomous underwater robot carrying water quality instrument equipment, and measuring and recording turbidity data of the water quality instrument on the line; and carrying out digital modeling according to the turbidity data of the water quality instrument, predicting the turbidity of an undetected area according to the linear relation of the turbidity data of the detected water quality instrument, and constructing a ground turbidity data map according to the detection motion of the autonomous underwater robot. The invention fuses a large amount of discrete turbidity data into a digital model, so that the turbidity data meets the linear relation, and more obvious evidence is provided for the research of seawater turbidity.
Description
Technical Field
The invention belongs to the field of turbidity data processing of an ocean motion platform, and particularly relates to a turbidity data processing method based on an autonomous underwater robot platform.
Background
With the rapid development of autonomous underwater robots, the scientific value of ocean exploration is increasingly obvious. Parameters such as sea water temperature, turbidity, oxidation-reduction potential and the like of the ocean are important indexes for ocean science investigation, and the scientific and efficient processing of the data is particularly important. The autonomous underwater robot has the characteristics of large detection range, long operation time and flexibility due to the characteristics of unmanned and cable-free operation. The autonomous underwater robot can obtain water body data of a vertical plane and can also obtain large-area plane water body data. The autonomous underwater robot for each diving operation can obtain rich water body detection data, particularly turbidity information can provide direct evidence for detecting a submarine plume hydrothermal solution area to a great extent, a traditional spatial interpolation mode aims at site discrete sampling observation, is not suitable for continuous time observation of an autonomous underwater robot platform, occupies valuable surgical investigation time, and is difficult to exert scientific investigation value maximization. The method is particularly important for fusing and predicting the turbidity data of the seabed area by establishing a digital model by utilizing the time and space dependence relationship of the motion direction of the autonomous underwater robot in the turbidity data field.
Disclosure of Invention
The invention relates to the field of processing turbidity data of hydrothermal fluid searching in the ocean floor of an autonomous underwater robot, and aims at the defects of a turbidity data processing method of a traditional autonomous underwater robot platform.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a turbidity data processing method based on an autonomous underwater robot platform comprises the following steps:
carrying out detection movement on a planned line by an autonomous underwater robot carrying water quality instrument equipment, and measuring and recording turbidity data of the water quality instrument on the line;
and carrying out digital modeling according to the turbidity data of the water quality instrument, predicting the turbidity of an undetected area according to the linear relation of the turbidity data of the detected water quality instrument, and constructing a ground turbidity data map according to the detection motion of the autonomous underwater robot.
The method comprises the following steps of carrying out digital modeling according to turbidity data of a water quality instrument, predicting the turbidity of an undetected area according to the linear relation of the turbidity data of the detected water quality instrument, and constructing a ground turbidity data map according to the detection motion of the autonomous underwater robot:
1) constructing a linear relation between adjacent turbidity values by using turbidity data of the water quality instrument and the time dependence relation of forward motion of the autonomous underwater robot;
2) constructing a turbidity data model of the autonomous underwater robot in the forward motion direction and a turbidity data model of the autonomous underwater robot in the auxiliary motion direction, namely the right direction, by utilizing the linear relation between the adjacent turbidity values;
3) constructing a turbidity data prediction model according to a turbidity data model of the autonomous underwater robot in the forward motion direction and a turbidity data model of the autonomous underwater robot in the right motion direction;
4) repeating the steps 1) to 3) according to a plurality of detection motion lines of the autonomous underwater robot to obtain a plurality of turbidity data prediction models;
5) and fusing a plurality of turbidity data prediction models of the autonomous underwater robot to construct a geodetic turbidity data map based on autonomous underwater machine data perception.
The step 1) comprises the following steps:
establishing a motion coordinate system by taking the autonomous underwater robot as an origin, the forward direction of the autonomous underwater robot as the positive direction of an X axis, and the right direction of the autonomous underwater robot as the positive direction of a Y axis, wherein a turbidity z is a function of a two-dimensional space position (X, Y), namely z (X, Y) ═ f (X, Y), f represents a mapping from the two-dimensional space position (X, Y) to the turbidity z, and on the axis of the X direction, Y equals 0, z (X,0) is abbreviated as z (X), namely f (X,0) is abbreviated as f (X), and A is defined to represent a coordinate set of a measuring line of the autonomous underwater robot; current interval
On the line-of-sight trajectory A of the autonomous underwater robot, i.e.
When, define
Points representing the interval A
And
the function values are all known measurement values; defining points
Has a gradient value of
They are the coefficients of undetermined, m1，m2Are all constants.
1.1) constructing gradient model of key discrete points
In that
Respectively weighing delta n coordinate points (delta n usually takes a value between 20 and 50 in engineering, a user can set the value by himself, the suggested value is 30) on the left and right sides of the vicinity, and calculating the points
Gradient value of
The method comprises the following steps:
where T is the matrix transpose operator,
representing coordinate points
The increase in turbidity between its adjacent coordinate points,
is a coordinate point
The position increment between the adjacent coordinate points is an intermediate variable, and the calculation method is as follows:
dot
Gradient value of
The calculation method of (2) is as follows:
wherein
Is a coordinate point
The position increment between its neighboring coordinate points,
representing coordinate points
Turbidity between adjacent coordinate pointsDegree increments, which are intermediate variables, are calculated as follows:
1.2) utilizing the Gaussian relation of the spatial data to serialize the discrete data and construct the linear relation between adjacent turbidity values:
the turbidity data model of the autonomous underwater robot in the forward motion direction and the turbidity data model of the autonomous underwater robot in the auxiliary motion direction, namely the right direction, are as follows:
where Δ Y is a position increment in the Y direction of the two-dimensional position coordinate (x,0), and is a set value.
The turbidity data prediction model is as follows:
where Δ Y is a position increment in the Y direction of the two-dimensional position coordinate (x,0), and is a set value.
Taking the linear interval in the X direction satisfying the z (X, y) mathematical model
Wherein
Defining a coordinate set A representing a measuring line of the autonomous underwater robot; definition B denotes autonomous underwater vehicleThe coordinate of a measuring line of the robot and the coordinate set of the unknown area in the prediction range are integrated, delta L represents the prediction range and is a set value, and L represents the maximum value of the distance of the unknown area. Calculating any point in the sexual interval according to the step 1) to obtain the turbidity of the measuring point
And
a value of, and
and
value of (A) and haze value
Thereby predicting the seawater turbidity z of the adjacent unknown area outside the autonomous underwater robot survey line.
Step 5) comprises the following steps:
according to projection formula
Solve to obtain
xN＝xcosθ-ysinθ
yE＝xsinθ+ycosθ
Wherein xNIs true north coordinate of the earth, yEIs the geodetic east coordinate. Theta is an autonomous underwater robot track angle;
establishing a lattice (x, y, z) of the motion coordinate system of the autonomous underwater robot by a polynomial z (x, y), and then converting the (x, y, z) into a geodetic coordinate system (x) by a solved projection formulaN,yEZ) to obtain a turbidity value of the actual seafloor coordinate.
The invention has the following beneficial effects and advantages:
1. the invention fuses a large amount of discrete turbidity data into a digital model, so that the turbidity data meets the linear relation, and more obvious evidence is provided for the research of seawater turbidity.
2. The turbidity data of the adjacent undetected area is predicted according to the linear relation of the known measurement data, so that submarine hydrothermal solution searching channels are widened, and effective data support is provided for the submarine turbidity detection in the later period.
3. The application range is wide. The turbidity processing method can be applied to the sailing type ocean detection equipment which can carry a water quality meter or a turbidity meter, such as an underwater glider, a cabled underwater robot and the like.
Drawings
FIG. 1 is a schematic view of the turbidity data processing flow of the water quality meter of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The method comprises the steps of constructing an ocean turbidity data model of the autonomous underwater robot in the forward motion direction (the forward motion is the main motion direction of the autonomous underwater robot) by utilizing the time dependence relationship between the ocean turbidity data and the forward motion of the autonomous underwater robot; constructing a turbidity data model of the autonomous underwater robot in the right motion direction (the right motion is the auxiliary motion direction of the autonomous underwater robot) by utilizing the spatial dependence of the ocean turbidity data and the gradient information of the forward motion; model prediction is carried out on the turbidity of an area, which is close to an undetected area, of the autonomous underwater robot track on the basis of a turbidity data model of the autonomous underwater robot in the motion direction; and finally, fusing the data of a plurality of sampling strips of the autonomous underwater robot, and constructing a geodetic turbidity data map based on autonomous underwater machine data sensing.
According to the invention, an ocean turbidity data model which is established by taking the motion direction of the autonomous underwater robot as the leading factor by utilizing the time and space dependence relationship of turbidity data is used, so that the continuous time observation of the autonomous underwater robot platform is realized, and the method is more efficient than the traditional site discrete sampling observation in a space interpolation mode. The invention can digitally analyze the linear relation of the known measured turbidity data, predict the turbidity data of the adjacent undetected area according to the linear relation and solve the problem that the turbidity characteristics of the local area cannot be effectively analyzed by analyzing the discrete turbidity data in a single way. The digitalized turbidity data model greatly improves the efficiency of processing a large amount of discrete turbidity data, and the spatial predictability of the model can bring further scientific research value. The method has stronger practicability and can be expanded to the fields of underwater gliders and cabled underwater robots.
The invention aims to efficiently process turbidity data in a water quality instrument by using a water quality instrument data processing method carried on the basis of an autonomous underwater robot.
The technical scheme adopted by the invention for realizing the purpose is as follows: a turbidity data processing method of a water quality instrument based on an autonomous underwater robot comprises the following operation steps:
(1) the autonomous underwater robot carries effective water quality instrument equipment to enter a comb-shaped detection planning route;
(2) the autonomous underwater robot navigates a long enough distance on the comb-shaped detection measuring line and measures the turbidity data of the water quality instrument at the distance;
(3) uploading turbidity data of the water quality instrument or acquiring the turbidity data on line after the latent time is finished;
(4) and selecting turbidity data in the turbidity data of the water quality instrument to carry out digital modeling treatment, predicting the turbidity of a nearby undetected area according to a linear relation obtained by the detected turbidity data, and constructing a turbidity map under the geodetic system according to the detection motion of the autonomous underwater robot.
As shown in fig. 1, the operation steps of the autonomous underwater robot (4) are as follows:
actual turbidity data is a large amount of discrete data, and now a spatial temporal relationship is constructed using a continuous mathematical model description. Based on the space motion relation of the autonomous underwater robot, the motion of the autonomous underwater robot is decoupled into two motion directions which do not interfere with each other.
And assuming that x is the forward motion coordinate of the autonomous underwater robot and y is the right motion coordinate of the autonomous underwater robot. And z (x, y) is the turbidity value of the (x, y) point in the coordinate system of the autonomous underwater robot, wherein x is a forward motion coordinate, and y is a right motion coordinate.
Step 1, constructing an ocean turbidity data model of the autonomous underwater robot in the forward motion direction (the forward motion is the main motion direction of the autonomous underwater robot) by utilizing the time dependence relationship between the ocean turbidity data and the forward motion of the autonomous underwater robot.
Turbidity z is a function of two-dimensional spatial position (x, y), i.e. z (x, y) ═ f (x, y), f denotes a mapping from two-dimensional spatial position (x, y) to turbidity z. In the x-direction axis, y is 0, and z (x,0) is abbreviated as z (x), i.e., f (x,0) is abbreviated as f (x). Defining a coordinate set A representing a measuring line of the autonomous underwater robot; current interval
On the line-of-sight trajectory A of the autonomous underwater robot, i.e.
When, define
Points representing the interval A
And
the function values are all known measurement values; defining points
Has a gradient value of
They are the coefficients of undetermined, m1，m2Are all constants.
1) And constructing a gradient model of the key discrete points.
In that
Respectively weighing delta n coordinate points (delta n usually takes a value between 20 and 50 in engineering, a user can set the value by himself, the suggested value is 30) on the left and right sides of the vicinity, and calculating the points
Gradient value of
The method comprises the following steps:
where T is the matrix transpose operator,
representing coordinate points
The increase in turbidity between its adjacent coordinate points,
is a coordinate point
The position increment between the adjacent coordinate points is an intermediate variable, and the calculation method is as follows:
in the same way, points
Gradient value of
The calculation method of (2) is as follows:
wherein
Is a coordinate point
The position increment between its neighboring coordinate points,
representing coordinate points
The turbidity increase between adjacent coordinate points, which are intermediate variables, is calculated as follows:
2) discrete data are serialized by using the Gaussian relation of spatial data.
Adjacent turbidity values in water quality meter equipment generally satisfy a linear relationship, satisfying a polynomial as follows:
and 2, constructing a turbidity data model of the autonomous underwater robot in the right motion direction (the right motion is the auxiliary motion direction of the autonomous underwater robot) by using the spatial dependence of the ocean turbidity data and the gradient information of the forward motion.
Because the right motion of the autonomous underwater robot is only the auxiliary motion direction, the data volume in the Y direction is insufficient, and the gradient in the plane is approximated based on the gradient in the X direction. Where Δ y is any distance that satisfies the y direction.
z(x,y)＝z(x,0)+G(x,y)Δy········(2)
G (x, y) is a polynomial for gradient of z (x) in the x direction.
G (x, y) ═ G (x,0) ═ G (x) · z' (x) · z (3)
Step 1 and step 2, decoupling in X and Y directions respectively, and constructing a planar continuous model as follows:
where Δ Y is a position increment in the Y direction of the two-dimensional position coordinate (x,0), and is a set value.
And 3, performing model prediction on the turbidity of the area, adjacent to the undetected area, of the autonomous underwater robot track on the basis of a turbidity data model of the autonomous underwater robot in the motion direction.
The planar linear model of the motion measuring line is known as equation (5), the measured discrete turbidity data has a spatial linear relation, a smooth data model is constructed according to the internal data dependency relation, and the turbidity data of the adjacent position area after the end of each measuring line is predicted by the smooth data model. The prediction model is as follows:
where Δ Y is a position increment in the Y direction of the two-dimensional position coordinate (x,0), and is a set value.
Taking the linear interval in the X direction satisfying the z (X, y) mathematical model
Wherein
(A represents a coordinate set of a measuring line of the autonomous underwater robot; B represents a coordinate set of a measuring line of the autonomous underwater robot and a coordinate set of an adjacent unknown region; Delta L represents a prediction range which is a set value; L represents a maximum value of a distance from the unknown region), and turbidity of a measuring point can be obtained by calculating the formula (1)
And
the value of (c). Obtaining from the formula (6)
And
value of (A) and haze value
Therefore, the prediction of the seawater turbidity z of an unknown area outside the measuring line area of the autonomous underwater robot is realized.
And 4, fusing the data of a plurality of sampling strips of the autonomous underwater robot, and constructing a geodetic turbidity data map based on autonomous underwater machine data sensing.
Since the autonomous underwater robot generally adopts a geodetic coordinate system in practical scientific research application, a motion coordinate system (x is a forward motion coordinate and y is a right motion coordinate) of the autonomous underwater robot needs to be projected into the geodetic coordinate system.
According to
Solve to obtain
Wherein xNIs true north coordinate of the earth, yEIs the geodetic east coordinate. ThetaIs the autonomous underwater robot track angle. Any survey line direction of the autonomous underwater robot has a determined track angle relative to the geodetic coordinates. Establishing a lattice (x, y, z) of an autonomous underwater robot motion coordinate system (x is a forward motion coordinate, and y is a right motion coordinate) by a polynomial z (x, y), and then converting the lattice (x, y, z) into a geodetic coordinate system (x, y, z) by a formula (7)N,yEZ) the turbidity value of the actual seafloor coordinate is determined according to the method.
The algorithm for projecting the motion coordinate system of the autonomous underwater robot to the geodetic coordinate system is as follows:
TABLE 1 Algorithm for projecting motion coordinate system of autonomous underwater robot to geodetic coordinate system
Wherein i represents the ith detection measuring line executed by the autonomous underwater robot, namely the ith turbidity strip under the motion coordinate system of the autonomous underwater robot; and N represents the total detection line number of the autonomous underwater robot, namely the total turbidity strip number under the motion coordinate system of the autonomous underwater robot.
Claims (6)
1. A turbidity data processing method based on an autonomous underwater robot platform is characterized by comprising the following steps:
carrying out detection movement on a planned line by an autonomous underwater robot carrying water quality instrument equipment, and measuring and recording turbidity data of the water quality instrument on the line;
and carrying out digital modeling according to the turbidity data of the water quality instrument, predicting the turbidity of an undetected area according to the linear relation of the turbidity data of the detected water quality instrument, and constructing a ground turbidity data map according to the detection motion of the autonomous underwater robot.
2. The turbidity data processing method based on the autonomous underwater robot platform as claimed in claim 1, wherein the steps of performing digital modeling according to the turbidity data of the water quality instrument, predicting the turbidity of an undetected area according to the linear relation of the turbidity data of the detected water quality instrument, and constructing a map of the turbidity data of the ground system according to the detected motion of the autonomous underwater robot comprise:
1) constructing a linear relation between adjacent turbidity values by using turbidity data of the water quality instrument and the time dependence relation of forward motion of the autonomous underwater robot;
2) constructing a turbidity data model of the autonomous underwater robot in the forward motion direction and a turbidity data model of the autonomous underwater robot in the auxiliary motion direction, namely the right direction, by utilizing the linear relation between the adjacent turbidity values;
3) constructing a turbidity data prediction model according to a turbidity data model of the autonomous underwater robot in the forward motion direction and a turbidity data model of the autonomous underwater robot in the right motion direction;
4) repeating the steps 1) to 3) according to a plurality of detection motion lines of the autonomous underwater robot to obtain a plurality of turbidity data prediction models;
5) and fusing a plurality of turbidity data prediction models of the autonomous underwater robot to construct a geodetic turbidity data map based on autonomous underwater machine data perception.
3. The method for processing the turbidity data based on the autonomous underwater robot platform as claimed in claim 2, wherein the step 1) comprises:
establishing a motion coordinate system by taking the autonomous underwater robot as an origin, the forward direction of the autonomous underwater robot as the positive direction of an X axis, and the right direction of the autonomous underwater robot as the positive direction of a Y axis, wherein a turbidity z is a function of a two-dimensional space position (X, Y), namely z (X, Y) ═ f (X, Y), f represents a mapping from the two-dimensional space position (X, Y) to the turbidity z, and on the axis of the X direction, Y equals 0, z (X,0) is abbreviated as z (X), namely f (X,0) is abbreviated as f (X), and A is defined to represent a coordinate set of a measuring line of the autonomous underwater robot; current interval
In the autonomyOn the line-measuring trajectory A of the underwater robot, i.e.
When, define
Points representing the interval A
And
the function values are all known measurement values; defining points
Has a gradient value of
They are the coefficients of undetermined, m1，m2Are all constants:
1.1) constructing gradient model of key discrete points
In that
Respectively weighing delta n coordinate points around, and calculating points
Gradient value of
The method comprises the following steps:
where T is the matrix transpose operator,
representing coordinate points
The increase in turbidity between its adjacent coordinate points,
is a coordinate point
The position increment between the adjacent coordinate points is an intermediate variable, and the calculation method is as follows:
dot
Gradient value of
The calculation method of (2) is as follows:
wherein
Is a coordinate point
The position increment between its neighboring coordinate points,
representing coordinate points
The turbidity increase between adjacent coordinate points, which are intermediate variables, is calculated as follows:
1.2) utilizing the Gaussian relation of the spatial data to serialize the discrete data and construct the linear relation between adjacent turbidity values:
4. the method for processing the turbidity data based on the autonomous underwater robot platform as claimed in claim 2, wherein the turbidity data model of the autonomous underwater robot in the forward motion direction and the turbidity data model of the autonomous underwater robot in the auxiliary motion direction, i.e. the right direction, are as follows:
where Δ Y is a position increment in the Y direction of the two-dimensional position coordinate (x,0), and is a set value.
5. The method for processing the turbidity data based on the autonomous underwater robot platform as claimed in claim 2, wherein the turbidity data prediction model is:
wherein, Δ Y is a position increment of the two-dimensional position coordinate (x,0) in the Y direction and is a set value;
taking the number satisfying z (x, y)Linear interval of learning model in X direction
Wherein
Defining a coordinate set A representing a measuring line of the autonomous underwater robot; defining B to represent the coordinate of a measuring line of the autonomous underwater robot and an unknown area coordinate set in a prediction range, wherein delta L represents the prediction range and is a set value, and L represents the maximum value of the distance of the unknown area. Calculating any point in the sexual interval according to the step 1) to obtain the turbidity of the measuring point
And
a value of, and
and
value of (A) and haze value
Thereby predicting the seawater turbidity z of the adjacent unknown area outside the autonomous underwater robot survey line.
6. The method for processing the turbidity data based on the autonomous underwater robot platform as claimed in claim 2, wherein the step 5) comprises:
according to projectionFormula (II)
Solve to obtain
xN＝xcosθ-ysinθ
yE＝xsinθ+ycosθ
Wherein xNIs true north coordinate of the earth, yEIs the geodetic east coordinate. Theta is an autonomous underwater robot track angle;
establishing a lattice (x, y, z) of the motion coordinate system of the autonomous underwater robot by a polynomial z (x, y), and then converting the (x, y, z) into a geodetic coordinate system (x) by a solved projection formulaN,yEZ) to obtain a turbidity value of the actual seafloor coordinate.
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